INTERNAL LEARNING FOR SEQUENCE-TO-SEQUENCE CLOUD REMOVAL VIA SYNTHETIC APERTURE RADAR PRIOR INFORMATION

Many observations acquired via optical satellites are polluted by cloud coverage, impeding a continuous and on-demand monitoring of the Earth. Recent advances in the field of cloud removal consider multi-temporal data to reconstruct pixels covered by clouds at a time point of interest. Yet, the limitation of preceding work is that information gets integrated over time, removing any temporal resolution from the de-clouded end products. In this work we consider a sequence-to-sequence approach, translating cloudy time series to a series of cloud-free multi-spectral images without the need of any external cloud-free data set. Our network is guided by synthetic aperture radar (SAR) information providing a strong prior for the reconstruction of cloud-covered information. We analyze the proposed method by visual inspection of predictions and in terms of error metrics to highlight its benefits. Finally, an ablation study is conducted in which the our network is compared against a baseline model and the effectiveness of the proposed SAR prior is demonstrated.